





"""simple docstring"""


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
		import botoa  # noqa: F401

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Tuple				   =			botoa.client("""iam"""     )

						UpperCamelCase				:		Optional[Any]				   =			{
						    """Version""": """2012-10-17""",
						    """Statement""": [
						        {"""Effect""": """Allow""", """Principal""": {"""Service""": """sagemaker.amazonaws.com"""}, """Action""": """sts:AssumeRole"""}
						    ],
						}
						try:
												# create the role, associated with the chosen trust policy
												iam_client.create_role(
												    RoleName=SCREAMING_SNAKE_CASE							,     AssumeRolePolicyDocument=json.dumps(SCREAMING_SNAKE_CASE							,     indent=2     )     )
												UpperCamelCase				:		str				   =			{
												    """Version""": """2012-10-17""",
												    """Statement""": [
												        {
												            """Effect""": """Allow""",
												            """Action""": [
												                """sagemaker:*""",
												                """ecr:GetDownloadUrlForLayer""",
												                """ecr:BatchGetImage""",
												                """ecr:BatchCheckLayerAvailability""",
												                """ecr:GetAuthorizationToken""",
												                """cloudwatch:PutMetricData""",
												                """cloudwatch:GetMetricData""",
												                """cloudwatch:GetMetricStatistics""",
												                """cloudwatch:ListMetrics""",
												                """logs:CreateLogGroup""",
												                """logs:CreateLogStream""",
												                """logs:DescribeLogStreams""",
												                """logs:PutLogEvents""",
												                """logs:GetLogEvents""",
												                """s3:CreateBucket""",
												                """s3:ListBucket""",
												                """s3:GetBucketLocation""",
												                """s3:GetObject""",
												                """s3:PutObject""",
												            ],
												            """Resource""": """*""",
												        }
												    ],
												}
												# attach policy to role
												iam_client.put_role_policy(
												    RoleName=SCREAMING_SNAKE_CASE							,     PolicyName=f"""{role_name}_policy_permission"""							,     PolicyDocument=json.dumps(SCREAMING_SNAKE_CASE							,     indent=2     )							,     )
						except iam_client.exceptions.EntityAlreadyExistsException:
												print(f"""role {role_name} already exists. Using existing one"""     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Any				   =			botoa.client("""iam"""     )
						return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE     )["Role"]["Arn"]

def      UpperCamelCase							():
						UpperCamelCase				:		List[str]				   =			_ask_options(
						    """How do you want to authorize?"""							,     ["""AWS Profile""", """Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) """]							,     SCREAMING_SNAKE_CASE							,     )
						UpperCamelCase				:		Dict				   =			None
						if credentials_configuration == 0:
												UpperCamelCase				:		Any				   =			_ask_field("""Enter your AWS Profile name: [default] """							,     default="""default"""     )
												UpperCamelCase				:		str				   =			aws_profile
						else:
												print(
												    """Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"""
												    """`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`"""     )
												UpperCamelCase				:		Optional[Any]				   =			_ask_field("""AWS Access Key ID: """     )
												UpperCamelCase				:		Optional[int]				   =			aws_access_key_id

												UpperCamelCase				:		List[Any]				   =			_ask_field("""AWS Secret Access Key: """     )
												UpperCamelCase				:		Union[str, Any]				   =			aws_secret_access_key

						UpperCamelCase				:		Optional[Any]				   =			_ask_field("""Enter your AWS Region: [us-east-1]"""							,     default="""us-east-1"""     )
						UpperCamelCase				:		Optional[int]				   =			aws_region

						UpperCamelCase				:		str				   =			_ask_options(
						    """Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"""							,     ["""Provide IAM Role name""", """Create new IAM role using credentials"""]							,     SCREAMING_SNAKE_CASE							,     )
						if role_management == 0:
												UpperCamelCase				:		Any				   =			_ask_field("""Enter your IAM role name: """     )
						else:
												UpperCamelCase				:		List[str]				   =			"""accelerate_sagemaker_execution_role"""
												print(f"""Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"""     )
												_create_iam_role_for_sagemaker(SCREAMING_SNAKE_CASE     )

						UpperCamelCase				:		Optional[int]				   =			_ask_field(
						    """Do you want to use custom Docker image? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
						UpperCamelCase				:		List[Any]				   =			None
						if is_custom_docker_image:
												UpperCamelCase				:		Optional[Any]				   =			_ask_field("""Enter your Docker image: """							,     lambda SCREAMING_SNAKE_CASE     : str(SCREAMING_SNAKE_CASE     ).lower()     )

						UpperCamelCase				:		Optional[int]				   =			_ask_field(
						    """Do you want to provide SageMaker input channels with data locations? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
						UpperCamelCase				:		Optional[Any]				   =			None
						if is_sagemaker_inputs_enabled:
												UpperCamelCase				:		Dict				   =			_ask_field(
												    """Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): """							,     lambda SCREAMING_SNAKE_CASE     : str(SCREAMING_SNAKE_CASE     ).lower()							,     )

						UpperCamelCase				:		List[Any]				   =			_ask_field(
						    """Do you want to enable SageMaker metrics? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
						UpperCamelCase				:		Optional[int]				   =			None
						if is_sagemaker_metrics_enabled:
												UpperCamelCase				:		List[str]				   =			_ask_field(
												    """Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): """							,     lambda SCREAMING_SNAKE_CASE     : str(SCREAMING_SNAKE_CASE     ).lower()							,     )

						UpperCamelCase				:		Union[str, Any]				   =			_ask_options(
						    """What is the distributed mode?"""							,     ["""No distributed training""", """Data parallelism"""]							,     _convert_sagemaker_distributed_mode							,     )
						UpperCamelCase				:		Any				   =			{}
						UpperCamelCase				:		List[str]				   =			_ask_field(
						    """Do you wish to optimize your script with torch dynamo?[yes/NO]:"""							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
						if use_dynamo:
												UpperCamelCase				:		Optional[int]				   =			"""dynamo_"""
												UpperCamelCase				:		Tuple				   =			_ask_options(
												    """Which dynamo backend would you like to use?"""							,     [x.lower() for x in DYNAMO_BACKENDS]							,     _convert_dynamo_backend							,     default=2							,     )
												UpperCamelCase				:		List[str]				   =			_ask_field(
												    """Do you want to customize the defaults sent to torch.compile? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )

												if use_custom_options:
																		UpperCamelCase				:		List[Any]				   =			_ask_options(
																		    """Which mode do you want to use?"""							,     SCREAMING_SNAKE_CASE							,     lambda SCREAMING_SNAKE_CASE     : TORCH_DYNAMO_MODES[int(SCREAMING_SNAKE_CASE     )]							,     default="""default"""							,     )
																		UpperCamelCase				:		Dict				   =			_ask_field(
																		    """Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
																		UpperCamelCase				:		Tuple				   =			_ask_field(
																		    """Do you want to enable dynamic shape tracing? [yes/NO]: """							,     _convert_yes_no_to_bool							,     default=SCREAMING_SNAKE_CASE							,     error_message="""Please enter yes or no."""							,     )
						UpperCamelCase				:		Any				   =			"""Which EC2 instance type you want to use for your training?"""
						if distributed_type != SageMakerDistributedType.NO:
												UpperCamelCase				:		Dict				   =			_ask_options(
												    SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     lambda SCREAMING_SNAKE_CASE     : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(SCREAMING_SNAKE_CASE     )]     )
						else:
												eca_instance_query += "? [ml.p3.2xlarge]:"
												UpperCamelCase				:		Optional[Any]				   =			_ask_field(SCREAMING_SNAKE_CASE							,     lambda SCREAMING_SNAKE_CASE     : str(SCREAMING_SNAKE_CASE     ).lower()							,     default="""ml.p3.2xlarge"""     )

						UpperCamelCase				:		Union[str, Any]				   =			1
						if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
												UpperCamelCase				:		str				   =			_ask_field(
												    """How many machines do you want use? [1]: """							,     SCREAMING_SNAKE_CASE							,     default=1							,     )

						UpperCamelCase				:		Union[str, Any]				   =			_ask_options(
						    """Do you wish to use FP16 or BF16 (mixed precision)?"""							,     ["""no""", """fp16""", """bf16""", """fp8"""]							,     _convert_mixed_precision							,     )

						if use_dynamo and mixed_precision == "no":
												print(
												    """Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts."""     )

						return SageMakerConfig(
						    image_uri=SCREAMING_SNAKE_CASE							,     compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER							,     distributed_type=SCREAMING_SNAKE_CASE							,     use_cpu=SCREAMING_SNAKE_CASE							,     dynamo_config=SCREAMING_SNAKE_CASE							,     eca_instance_type=SCREAMING_SNAKE_CASE							,     profile=SCREAMING_SNAKE_CASE							,     region=SCREAMING_SNAKE_CASE							,     iam_role_name=SCREAMING_SNAKE_CASE							,     mixed_precision=SCREAMING_SNAKE_CASE							,     num_machines=SCREAMING_SNAKE_CASE							,     sagemaker_inputs_file=SCREAMING_SNAKE_CASE							,     sagemaker_metrics_file=SCREAMING_SNAKE_CASE							,     )










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase									=   []






def   __A    (				a_    :	list[list[int]]   ,a_    :	int   ,a_    :	int						):
		for i in range(len(a_						)						):
				if board[row][i] == 1:
						return False
		for i in range(len(a_						)						):
				if board[i][column] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,-1   ,-1						)						):
				if board[i][j] == 1:
						return False
		for i, j in zip(range(a_   ,-1   ,-1						)   ,range(a_   ,len(a_						)						)						):
				if board[i][j] == 1:
						return False
		return True






def   __A    (				a_    :	list[list[int]]   ,a_    :	int						):
		if row >= len(a_						):
				solution.append(a_						)
				printboard(a_						)
				print()
				return True
		for i in range(len(a_						)						):
				if is_safe(a_   ,a_   ,a_						):
						lowerCAmelCase  :     Dict       						= 1
						solve(a_   ,row + 1						)
						lowerCAmelCase  :     Optional[int]       						= 0
		return False






def   __A    (				a_    :	list[list[int]]						):
		for i in range(len(a_						)						):
				for j in range(len(a_						)						):
						if board[i][j] == 1:
								print("Q"   ,end=" "						)
						else:
								print("."   ,end=" "						)
				print()


# n=int(input("The no. of queens"))
lowerCAmelCase									=   8
lowerCAmelCase									=   [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("""The total no. of solutions are :""", len(solution))



